In this paper, a short-circuit fault-tolerant operation control strategy of dual-winding permanent-magnet motor (DWPMM) under the four-quadrant condition for electrical flight surface actuator application is proposed and investigated. By the comparative analysis of the current fault-tolerant permanent-magnet motor drive system, it can be concluded that the DWPMM drive system is suitable for electrical flight surface actuator application. However, the current DWPMM drive system lacks the research on short-circuit fault-tolerant operation under the four-quadrant condition. Then, the electrical flight surface actuator system based on the DWPMM and short-circuit fault-tolerant operation control strategy under the four-quadrant condition is proposed and investigated. The experimental results show that the electrical flight surface actuator system based on the DWPMM can achieve the high performance of the four-quadrant fault-tolerant control with short-circuit fault under the four-quadrant operational condition.

With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy, precision, recall, and F1 score of the PT-TNNet model for equipment state monitoring and fault diagnosis. Finally, using the efficient PT-TNNet, Random Forest, and Extra Trees models, the cross-validation of the accuracy of winding temperature and insulation oil temperature of transformers is performed, confirming the superiority of the PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key sequential control systems, and the optimization of one-key sequential control system performance.

In this paper, the dual-stator axial-field flux-switching permanent magnet (DSAFFSPM) motors with E- and U-core stator modular segments, as well as different stator/rotor-pole combinations are compared. The operation performance of the DSAFFSPM machines are explored by using the MMF-permeance model method. A more comprehensive theoretical analysis, no more limited to numerical calculation, is presented. This reveals directly the relations between the operation performance and design parameters of the machine. The performance comparisons between the E-shaped and U-shaped teeth DSAFFSPM machines are given, including the effects of the structural parameters on the electromagnetic torque, different stator/rotor-pole combinations on the winding factor and gear ratio, the differences of the average torque and the torque density and the efficiency in the 12/10 U-core and 6/10 E-core topology. These are the basis of the DSAFFSPM machine design and optimization. Finally, the experiments on the two prototype, 6/10 E-core and 12/10 U-core DSAFFSPM machines are carried out. The results show that the average torque of the 12/10 U-core prototype is higher than that of the 6/10 E-core prototype, while the torque density and the efficiency of 6/10 E-core DSAFFSPM machines are much higher.

Hybrid axial field flux‐switching permanent magnet (HAFFSPM) machine exhibits a high‐cogging torque due to its doubly salient structure and flux focusing effect. In consideration of the armature and field windings are both equipped in the HAFFSPM machine, a cogging torque reduction method by the harmonic field current injection is proposed in this study. Initially, the mathematical model of the HAFFSPM machine is analysed. Moreover, the theoretical principle of the proposed method is investigated based on the analytical model of the machine. The amplitude and phase angle of the injected current are calculated according to the harmonic component of the cogging torque. The feasibility of the proposed method is analysed and validated by both the finite‐element analysis method and experiments on the prototype. Finally, the comparison between the proposed method and several topology optimisation methods of the HAFFSPM machine is given to indicate the priority of the harmonic field current injection method on cogging torque reduction.

As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.

This paper gives a comprehensive comparison among two fault-tolerant axial field modular flux-switching (AFFSPM) machines with different stator modular segments (U- and E-core) and stator-slots/pole-pairs combinations. The topologies of two AFFSPM machines are introduced, each with two feasible stator slots and rotor pole-pairs combinations with high winding factors based on the slot-conductor back-EMF star vectors theory. Then, the static performance including the air-gap flux density, flux linkage, back-electromagnetic force (back-EMF), and electromagnetic torque are analyzed and compared. Moreover, the fault-tolerant capability is then investigated by the torque performance under one- and two-phase open-circuit conditions in which the corresponding fault-tolerant control strategies are applied. The predicted results confirm that the 6-stator slot/11-rotor-pole-pair E-core AFFSPM machine exhibits the best performance of the four candidates.